Analysis of biomolecular data: a post-genomic reflection.
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Computational Biology↗
Biomedical subjects
David K Y Chiu
Publications and source records attributed to David K Y Chiu.
Clustering and re-clustering for pattern discovery in gene expression data.
The combined interpretation of gene expression data and gene sequences is important for the investigation of the intricate relationships of gene expression at the transcription level. The expression data produced by microarray hybridization experiments can lead to the identification of clusters of co-expressed genes that are likely co-regulated by the same regulatory mechanisms. By analyzing the promoter regions of co-expressed genes, the common regulatory patterns characterized by transcription factor binding sites can be revealed. Many clustering algorithms have been used to uncover inherent clusters in gene expression data. In this paper, based on experiments using simulated and real data, we show that the performance of these algorithms could be further improved. For the clustering of expression data typically characterized by a lot of noise, we propose to use a two-phase clustering algorithm consisting of an initial clustering phase and a second re-clustering phase. The proposed algorithm has several desirable features: (i) it utilizes both local and global information by computing both a "local" pairwise distance between two gene expression profiles in Phase 1 and a "global" probabilistic measure of interestingness of cluster patterns in Phase 2, (ii) it distinguishes between relevant and irrelevant expression values when performing re-clustering, and (iii) it makes explicit the patterns discovered in each cluster for possible interpretations. Experimental results show that the proposed algorithm can be an effective algorithm for discovering clusters in the presence of very noisy data. The patterns that are discovered in each cluster are found to be meaningful and statistically significant, and cannot otherwise be easily discovered. Based on these discovered patterns, genes co-expressed under the same experimental conditions and range of expression levels have been identified and evaluated. When identifying regulatory patterns at the promoter regions of the co-expressed genes, we also discovered well-known transcription factor binding sites in them. These binding sites can provide explanations for the co-expressed patterns.
Algorithms↗
A multiple-pattern biosequence analysis method for diverse source association mining.
BACKGROUND: In order to understand the intricacy of biomolecules more comprehensively, significant patterns extracted from related data collected from diverse sources must be integrated. These data sources may be local or distributed, possibly with different representation schemes. Often, related data from different sources correspond only with respect to some of their values. METHODS: In biological sequence analysis, a goal is to identify new, previously unknown, relevant patterns, to obtain additional insights into the biomolecule. This is known as a pattern discovery task, rather than a pattern matching task. In this research, we present a method to tackle this problem typically found in molecular sequence analysis when the alignment of the sequences is represented as a relation. In this article, we propose an information measure to select attribute values that reflect multiple patterns of significant interdependence information. Based on these selected values, the patterns are evaluated with data values from other sources. RESULTS: In the experiments, a cancer-suppressor gene known as TP53 (encoding tumour protein p53) is analysed with the mutation records of patients. The experiments identify previously unknown points in the molecule that have patterns negatively associated with the occurrence of cancer. CONCLUSION: Since the evaluated interdependence pattern is a global property of the molecule, we conjecture that the identified points might also be a reflection of the molecule's cancer-suppressor characteristics. The experiments also confirm the usefulness of the proposed method.
Amino Acid Sequence↗